Abstract
Executing data mining algorithms locally is usually computationally intensive. A promising solution is to outsource the heavy data mining tasks and datasets. On the other hand, combining data from multiple institutions for a big and varied training set helps enhance the performance of data mining. Due to privacy concerns, different institutions should encrypt their datasets with different keys. Support Vector Machine (SVM) is a popular classifier. It is challenging to train SVM on encrypted datasets in the cloud. Existing schemes use either the multikey fully homomorphic encryption on one server, or partially homomorphic encryption on two non-colluding servers. The former is inefficient and the institutions have to remain online, while the latter relies too heavily on the assumption of two non-colluding servers. To remove these limitations, we demonstrate how to train SVM for both horizontally and vertically partitioned datasets. To reduce training complexity and enhance security, we focus on reduced SVM with a secure kernel matrix. We proved the security of our scheme and the experimental results validated its efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LIBSVM: A library for support vector machines. https://www.csie.ntu.edu.tw/~cjlin/libsvm/. Accessed 15 Apr 2020
Amirbekyan, A., Estivill-Castro, V.: A new efficient privacy-preserving scalar product protocol. In: Proceedings of the Sixth Australasian Conference on Data Mining and Analytics, vol. 70, pp. 209–214 (2007)
Blaze, M., Bleumer, G., Strauss, M.: Divertible protocols and atomic proxy cryptography. In: Advances in Cryptology EUROCRYPT 1998, pp. 127–144 (1998)
Boneh, D., Goh, E.J., Nissim, K.: Evaluating 2-DNF formulas on ciphertexts. In: Theory of Cryptography Conference, pp. 325–341 (2005)
Demmler, D., Schneider, T., Zohner, M.: ABY-A framework for efficient mixed-protocol secure two-party computation. In: NDSS (2015)
Dennis Jr., J.E., Schnabel, R.B.: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. SIAM, Philadelphia (1996)
Goldreich, O.: Foundations of Cryptography: Volume 2, Basic Applications. Cambridge University Press, New York (2004)
Hesamifard, E., Takabi, H., Ghasemi, M.: Deep neural networks classification over encrypted data. In: Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy (CODASPY 2019), pp. 97–108 (2019)
Laur, S., Lipmaa, H., Mielikäinen, T.: Cryptographically private support vector machines. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 618–624 (2006)
Lee, Y.J., Huang, S.Y.: Reduced support vector machines: A statistical theory. IEEE Trans. Neural Netw. 18(1), 1–13 (2007)
Lee, Y.J., Mangasarian, O.L.: RSVM: Reduced support vector machines. In: Proceedings of the 2001 SIAM International Conference on Data Mining, pp. 1–17 (2001)
Lee, Y.J., Mangasarian, O.L.: Ssvm: A smooth support vector machine for classification. Comput. Optim. Appl. 20(1), 5–22 (2001)
Li, X., Zhu, Y., Wang, J.: Secure Naïve Bayesian classification over encrypted data in cloud. In: Chen, L., Han, J. (eds.) ProvSec 2016. LNCS, vol. 10005, pp. 130–150. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47422-9_8
Li, X., Zhu, Y., Wang, J., Liu, Z., Liu, Y., Zhang, M.: On the soundness and security of privacy-preserving SVM for outsourcing data classification. IEEE Trans. Dependable Secure Comput. 15(5), 906–912 (2018)
Lin, K.-P., Chang, Y.-W., Chen, M.-S.: Secure support vector machines outsourcing with random linear transformation. Knowl. Inf. Syst. 44(1), 147–176 (2014). https://doi.org/10.1007/s10115-014-0751-1
Lin, K.P., Chen, M.S.: Privacy-preserving outsourcing support vector machines with random transformation. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 363–372 (2010)
Lin, K.M., Lin, C.J.: A study on reduced support vector machines. IEEE Trans. Neural Netw. 14(6), 1449–1459 (2003)
Liu, F., Ng, W.K., Zhang, W.: Encrypted SVM for outsourced data mining. In: EEE 8th International Conference on Cloud Computing, pp. 1085–1092 (2015)
Liu, F., Ng, W.K., Zhang, W.: Secure scalar product for big-data in mapreduce. In: IEEE International Conference on Big Data Computing Service and Applications (BigDataService), pp. 120–129 (2015)
Liu, L., Chen, R., Liu, X., Su, J., Qiao, L.: Towards practical privacy-preserving decision tree training and evaluation in the cloud. IEEE Trans. Inf. Forensics Secur. 15, 2914–2929 (2020)
López-Alt, A., Tromer, E., Vaikuntanathan, V.: On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption. In: Proceedings of the Forty-Fourth Annual ACM Symposium on Theory of Computing, pp. 1219–1234 (2012)
Ma, X., Zhang, F., Chen, X., Shen, J.: Privacy preserving multi-party computation delegation for deep learning in cloud computing. Inf. Sci. 459, 103–116 (2018)
Maekawa, T., Kawamura, A., Kinoshita, Y., Kiya, H.: Privacy-preserving SVM computing in the encrypted domain. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 897–902 (2018)
Mangasarian, O.L., Wild, E.W., Fung, G.M.: Privacy-preserving classification of vertically partitioned data via random kernels. ACM Trans. Knowl. Discovery Data (TKDD) 2(3), 12 (2008)
Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: IEEE Symposium on Security and Privacy (SP), pp. 334–348 (2013)
Pedersen, T.B., Saygın, Y., Savaş, E.: Secret sharing vs. encryption-based techniques for privacy preserving data mining (2007)
Peter, A., Tews, E., Katzenbeisser, S.: Efficiently outsourcing multiparty computation under multiple keys. IEEE Trans. Inf. Forensics Secur. 8(12), 2046–2058 (2013)
Qiu, S., Wang, B., Li, M., Liu, J., Shi, Y.: Toward practical privacy-preserving frequent itemset mining on encrypted cloud data. IEEE Trans. Cloud Comput. 8(1), 312–323 (2020)
Vaidya, J., Yu, H., Jiang, X.: Privacy-preserving SVM classification. Knowl. Inf. Syst. 14(2), 161–178 (2008)
Wang, B., Li, M., Chow, S.S., Li, H.: A tale of two clouds: Computing on data encrypted under multiple keys. In: 2014 IEEE Conference on Communications and Network Security (CNS), pp. 337–345 (2014)
Yu, A., Lai, W.L., Payor, J.: Efficient integer vector homomorphic encryption (2015). https://courses.csail.mit.edu/6.857/2015/files/yu-lai-payor.pdf
Zhang, J., Wang, X., Yiu, S.M., Jiang, Z.L., Li, J.: Secure dot product of outsourced encrypted vectors and its application to SVM. In: Proceedings of the Fifth ACM International Workshop on Security in Cloud Computing, pp. 75–82 (2017)
Zou, Y., Zhao, Z., Shi, S., Wang, L., Peng, Y., Ping, Y., Wang, B.: Highly secure privacy-preserving outsourced k-means clustering under multiple keys in cloud computing. Secur. Commun. Netw. 2020, 1–11 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, J., Yiu, S.M., Jiang, Z.L. (2020). Outsourced Privacy-Preserving Reduced SVM Among Multiple Institutions. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-60239-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60238-3
Online ISBN: 978-3-030-60239-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)